CS162 Operating Systems and Systems Programming Lecture 7 Synchronization (Continued) February 11 th, 2015 Prof. John Kubiatowicz

CS162Operating Systems andSystems Programming

Lecture 7

Synchronization (Continued)

February 11th, 2015Prof. John Kubiatowicz

http://cs162.eecs.Berkeley.edu

Lec 7.22/11/15 Kubiatowicz CS162 ©UCB Spring 2015

Recall: How does Thread get started?

• Eventually, run_new_thread() will select this TCB and return into beginning of ThreadRoot()– This really starts the new thread

Sta

ck g

row

thA

B(while)

yield

run_new_thread

switch

ThreadRoot

Other Thread

ThreadRoot stub

New Thread


Goals for Today

• Synchronization Operations• Higher-level Synchronization Abstractions

– Semaphores, monitors, and condition variables

• Programming paradigms for concurrent programs

Note: Some slides and/or pictures in the following areadapted from slides ©2005 Silberschatz, Galvin, and Gagne

Note: Some slides and/or pictures in the following areadapted from slides ©2005 Silberschatz, Galvin, and Gagne. Many slides generated from my lecture notes by Kubiatowicz.


Correctness for systems with concurrent threads

• If dispatcher can schedule threads in any way, programs must work under all circumstances– Can you test for this?– How can you know if your program works?

• Independent Threads:– No state shared with other threads– Deterministic Input state determines results– Reproducible Can recreate Starting Conditions,

I/O– Scheduling order doesn’t matter (if switch()

works!!!)• Cooperating Threads:

– Shared State between multiple threads– Non-deterministic– Non-reproducible

• Non-deterministic and Non-reproducible means that bugs can be intermittent– Sometimes called “Heisenbugs”


Interactions Complicate Debugging• Is any program truly independent?

– Every process shares the file system, OS resources, network, etc

– Extreme example: buggy device driver causes thread A to crash “independent thread” B

• You probably don’t realize how much you depend on reproducibility:– Example: Evil C compiler

» Modifies files behind your back by inserting errors into C program unless you insert debugging code

– Example: Debugging statements can overrun stack• Non-deterministic errors are really difficult to

find– Example: Memory layout of kernel+user programs

» depends on scheduling, which depends on timer/other things

» Original UNIX had a bunch of non-deterministic errors

– Example: Something which does interesting I/O» User typing of letters used to help generate secure

keys


Why allow cooperating threads?

• People cooperate; computers help/enhance people’s lives, so computers must cooperate– By analogy, the non-reproducibility/non-

determinism of people is a notable problem for “carefully laid plans”

• Advantage 1: Share resources– One computer, many users– One bank balance, many ATMs

» What if ATMs were only updated at night?– Embedded systems (robot control: coordinate arm

& hand)• Advantage 2: Speedup

– Overlap I/O and computation» Many different file systems do read-ahead

– Multiprocessors – chop up program into parallel pieces

• Advantage 3: Modularity – More important than you might think– Chop large problem up into simpler pieces

» To compile, for instance, gcc calls cpp | cc1 | cc2 | as | ld

» Makes system easier to extend


High-level Example: Web Server

• Server must handle many requests• Non-cooperating version:

serverLoop() { con = AcceptCon(); ProcessFork(ServiceWebPage(),con);

}• What are some disadvantages of this

technique?


Threaded Web Server

• Now, use a single process• Multithreaded (cooperating) version:

serverLoop() { connection = AcceptCon(); ThreadFork(ServiceWebPage(),connection);

}• Looks almost the same, but has many

advantages:– Can share file caches kept in memory, results of CGI

scripts, other things– Threads are much cheaper to create than

processes, so this has a lower per-request overhead• Question: would a user-level (say one-to-many)

thread package make sense here?– When one request blocks on disk, all block…

• What about Denial of Service attacks or digg / Slash-dot effects?


Thread Pools• Problem with previous version: Unbounded

Threads– When web-site becomes too popular –

throughput sinks• Instead, allocate a bounded “pool” of worker

threads, representing the maximum level of multiprogramming

master() { allocThreads(worker,queue); while(TRUE) { con=AcceptCon(); Enqueue(queue,con); wakeUp(queue); }}

worker(queue) { while(TRUE) { con=Dequeue(queue); if (con==null) sleepOn(queue); else ServiceWebPage(con); }}

MasterThread

Thread Pool

qu

eu

e


ATM Bank Server

• ATM server problem:– Service a set of requests– Do so without corrupting database– Don’t hand out too much money


ATM bank server example• Suppose we wanted to implement a server

process to handle requests from an ATM network:BankServer() { while (TRUE) { ReceiveRequest(&op, &acctId, &amount); ProcessRequest(op, acctId, amount); }}ProcessRequest(op, acctId, amount) { if (op == deposit) Deposit(acctId, amount); else if …}Deposit(acctId, amount) { acct = GetAccount(acctId); /* may use disk I/O */ acct->balance += amount; StoreAccount(acct); /* Involves disk I/O */}

• How could we speed this up?– More than one request being processed at once– Event driven (overlap computation and I/O)– Multiple threads (multi-proc, or overlap comp

and I/O)


Event Driven Version of ATM server• Suppose we only had one CPU

– Still like to overlap I/O with computation– Without threads, we would have to rewrite in

event-driven style• Example

BankServer() { while(TRUE) { event = WaitForNextEvent(); if (event == ATMRequest) StartOnRequest(); else if (event == AcctAvail) ContinueRequest(); else if (event == AcctStored) FinishRequest(); }}

– What if we missed a blocking I/O step?– What if we have to split code into hundreds of

pieces which could be blocking?– This technique is used for graphical

programming


Can Threads Make This Easier?

• Threads yield overlapped I/O and computation without “deconstructing” code into non-blocking fragments– One thread per request

• Requests proceeds to completion, blocking as required:

Deposit(acctId, amount) { acct = GetAccount(actId); /* May use disk I/O */ acct->balance += amount; StoreAccount(acct); /* Involves disk I/O */

}

• Unfortunately, shared state can get corrupted:Thread 1 Thread 2

load r1, acct->balanceload r1, acct->balanceadd r1, amount2store r1, acct->balance

add r1, amount1store r1, acct->balance


Review: Multiprocessing vs Multiprogramming

• What does it mean to run two threads “concurrently”?– Scheduler is free to run threads in any order and

interleaving: FIFO, Random, …– Dispatcher can choose to run each thread to

completion or time-slice in big chunks or small chunks

• Also recall: Hyperthreading– Possible to interleave threads on a per-

instruction basis– Keep this in mind for our examples (like

multiprocessing)

A B C

BA ACB C BMultiprogramming

ABC

Multiprocessing


Problem is at the lowest level• Most of the time, threads are working on

separate data, so scheduling doesn’t matter:Thread A Thread Bx = 1; y = 2;

• However, What about (Initially, y = 12):Thread A Thread Bx = 1; y = 2;x = y+1; y = y*2;

– What are the possible values of x? • Or, what are the possible values of x below?

Thread A Thread Bx = 1; x = 2;

– X could be 1 or 2 (non-deterministic!)– Could even be 3 for serial processors:

» Thread A writes 0001, B writes 0010. » Scheduling order ABABABBA yields 3!


Atomic Operations• To understand a concurrent program, we need

to know what the underlying indivisible operations are!

• Atomic Operation: an operation that always runs to completion or not at all– It is indivisible: it cannot be stopped in the middle

and state cannot be modified by someone else in the middle

– Fundamental building block – if no atomic operations, then have no way for threads to work together

• On most machines, memory references and assignments (i.e. loads and stores) of words are atomic– Consequently – weird example that produces “3”

on previous slide can’t happen• Many instructions are not atomic

– Double-precision floating point store often not atomic

– VAX and IBM 360 had an instruction to copy a whole array


• Threaded programs must work for all interleavings of thread instruction sequences– Cooperating threads inherently non-

deterministic and non-reproducible– Really hard to debug unless carefully designed!

• Example: Therac-25– Machine for radiation therapy

» Software control of electronaccelerator and electron beam/Xray production

» Software control of dosage– Software errors caused the

death of several patients» A series of race conditions on

shared variables and poor software design

» “They determined that data entry speed during editing was the key factor in producing the error condition: If the prescription data was edited at a fast pace, the overdose occurred.”

Correctness Requirements


Space Shuttle Example• Original Space Shuttle launch aborted 20

minutes before scheduled launch• Shuttle has five computers:

– Four run the “Primary Avionics Software System” (PASS)

» Asynchronous and real-time» Runs all of the control systems» Results synchronized and compared every 3 to 4

ms– The Fifth computer is the “Backup Flight

System” (BFS)» stays synchronized in case it is needed» Written by completely different team than PASS

• Countdown aborted because BFS disagreed with PASS– A 1/67 chance that PASS was out of sync one

cycle– Bug due to modifications in initialization code of

PASS» A delayed init request placed into timer queue» As a result, timer queue not empty at expected

time to force use of hardware clock– Bug not found during extensive simulation

PASS

BFS


Another Concurrent Program Example

• Two threads, A and B, compete with each other– One tries to increment a shared counter– The other tries to decrement the counter

Thread A Thread Bi = 0; i = 0;while (i < 10) while (i > -10) i = i + 1; i = i – 1;printf(“A wins!”); printf(“B wins!”);

• Assume that memory loads and stores are atomic, but incrementing and decrementing are not atomic

• Who wins? Could be either• Is it guaranteed that someone wins? Why or

why not?• What if both threads have their own CPU

running at same speed? Is it guaranteed that it goes on forever?


Hand Simulation Multiprocessor Example

• Inner loop looks like this:Thread A Thread B

r1=0 load r1, M[i]r1=0 load r1, M[i]

r1=1 add r1, r1, 1r1=-1 sub r1, r1, 1

M[i]=1 store r1, M[i]M[i]=-1 store r1, M[i]

• Hand Simulation:– And we’re off. A gets off to an early start– B says “hmph, better go fast” and tries really

hard– A goes ahead and writes “1”– B goes and writes “-1”– A says “HUH??? I could have sworn I put a 1

there”• Could this happen on a uniprocessor?

– Yes! Unlikely, but if you are depending on it not happening, it will and your system will break…


Administrivia

• Don’t Forget New Section!– Thursday 12-1, 320 Soda Hall– Need to know your TA!

• Sorry about HW 1– Got a little longer than we expected– Due next Monday! (HW 2 not handed out until

Monday)• No class on Monday! (Holiday)


Motivation: “Too much milk”

• Great thing about OS’s – analogy between problems in OS and problems in real life– Help you understand real life problems

better– But, computers are much stupider than

people• Example: People need to coordinate:

Arrive home, put milk away

3:30Buy milk3:25Arrive at storeArrive home, put milk

away3:20

Leave for storeBuy milk3:15

Leave for store3:05Look in Fridge. Out of milk

3:00

Look in Fridge. Out of milk

Arrive at store3:10

Person BPerson ATime


Definitions

• Synchronization: using atomic operations to ensure cooperation between threads– For now, only loads and stores are atomic– We are going to show that its hard to build

anything useful with only reads and writes• Mutual Exclusion: ensuring that only one

thread does a particular thing at a time– One thread excludes the other while doing its

task• Critical Section: piece of code that only one

thread can execute at once. Only one thread at a time will get into this section of code.– Critical section is the result of mutual exclusion– Critical section and mutual exclusion are two

ways of describing the same thing.


More Definitions• Lock: prevents someone from doing something

– Lock before entering critical section and before accessing shared data

– Unlock when leaving, after accessing shared data

– Wait if locked» Important idea: all synchronization involves

waiting• For example: fix the milk problem by putting a

key on the refrigerator– Lock it and take key if you are going to go buy

milk– Fixes too much: roommate angry if only wants

OJ

– Of Course – We don’t know how to make a lock yet

#$@%@#$@


Too Much Milk: Correctness Properties

• Need to be careful about correctness of concurrent programs, since non-deterministic– Always write down behavior first– Impulse is to start coding first, then when it

doesn’t work, pull hair out– Instead, think first, then code

• What are the correctness properties for the “Too much milk” problem???– Never more than one person buys– Someone buys if needed

• Restrict ourselves to use only atomic load and store operations as building blocks


Too Much Milk: Solution #1• Use a note to avoid buying too much milk:

– Leave a note before buying (kind of “lock”)– Remove note after buying (kind of “unlock”)– Don’t buy if note (wait)

• Suppose a computer tries this (remember, only memory read/write are atomic):

if (noMilk) { if (noNote) { leave Note; buy milk; remove note; }

}• Result?

– Still too much milk but only occasionally!– Thread can get context switched after checking

milk and note but before buying milk!• Solution makes problem worse since fails

intermittently– Makes it really hard to debug…– Must work despite what the dispatcher does!


Too Much Milk: Solution #1½

• Clearly the Note is not quite blocking enough– Let’s try to fix this by placing note first

• Another try at previous solution:

leave Note;if (noMilk) {

if (noNote) { leave Note; buy milk; }

}remove note;

• What happens here?– Well, with human, probably nothing bad– With computer: no one ever buys milk


Too Much Milk Solution #2• How about labeled notes?

– Now we can leave note before checking• Algorithm looks like this:

Thread A Thread Bleave note A; leave note B;if (noNote B) { if (noNoteA) { if (noMilk) { if (noMilk) { buy Milk; buy Milk; } }} }remove note A; remove note B;

• Does this work?• Possible for neither thread to buy milk

– Context switches at exactly the wrong times can lead each to think that the other is going to buy

• Really insidious: – Extremely unlikely that this would happen, but

will at worse possible time– Probably something like this in UNIX


Too Much Milk Solution #2: problem!

• I’m not getting milk, You’re getting milk

• This kind of lockup is called “starvation!”


Too Much Milk Solution #3• Here is a possible two-note solution:

Thread A Thread Bleave note A; leave note B;while (note B) { //X if (noNote A) { //Y do nothing; if (noMilk) {} buy milk;if (noMilk) { } buy milk; }} remove note B;remove note A;

• Does this work? Yes. Both can guarantee that: – It is safe to buy, or– Other will buy, ok to quit

• At X: – if no note B, safe for A to buy, – otherwise wait to find out what will happen

• At Y: – if no note A, safe for B to buy– Otherwise, A is either buying or waiting for B to

quit


Solution #3 discussion

• Our solution protects a single “Critical-Section” piece of code for each thread:

if (noMilk) { buy milk;

}• Solution #3 works, but it’s really unsatisfactory

– Really complex – even for this simple an example» Hard to convince yourself that this really works

– A’s code is different from B’s – what if lots of threads?

» Code would have to be slightly different for each thread

– While A is waiting, it is consuming CPU time» This is called “busy-waiting”

• There’s a better way– Have hardware provide better (higher-level)

primitives than atomic load and store– Build even higher-level programming abstractions

on this new hardware support


Too Much Milk: Solution #4• Suppose we have some sort of implementation

of a lock (more in a moment). – Lock.Acquire() – wait until lock is free, then grab– Lock.Release() – Unlock, waking up anyone

waiting– These must be atomic operations – if two threads

are waiting for the lock and both see it’s free, only one succeeds to grab the lock

• Then, our milk problem is easy:milklock.Acquire();if (nomilk) buy milk;milklock.Release();

• Once again, section of code between Acquire() and Release() called a “Critical Section”

• Of course, you can make this even simpler: suppose you are out of ice cream instead of milk– Skip the test since you always need more ice

cream.


Where are we going with synchronization?

• We are going to implement various higher-level synchronization primitives using atomic operations– Everything is pretty painful if only atomic

primitives are load and store– Need to provide primitives useful at user-level

Load/Store Disable Ints Test&Set Comp&Swap

Locks Semaphores Monitors Send/Receive

Shared Programs

Hardware

Higher-level API

Programs


How to implement Locks?• Lock: prevents someone from doing something

– Lock before entering critical section and before accessing shared data

– Unlock when leaving, after accessing shared data– Wait if locked

» Important idea: all synchronization involves waiting» Should sleep if waiting for a long time

• Atomic Load/Store: get solution like Milk #3– Looked at this last lecture– Pretty complex and error prone

• Hardware Lock instruction– Is this a good idea?– What about putting a task to sleep?

» How do you handle the interface between the hardware and scheduler?

– Complexity?» Done in the Intel 432» Each feature makes hardware more complex and slow


• How can we build multi-instruction atomic operations?– Recall: dispatcher gets control in two ways.

» Internal: Thread does something to relinquish the CPU

» External: Interrupts cause dispatcher to take CPU– On a uniprocessor, can avoid context-switching

by:» Avoiding internal events (although virtual memory

tricky)» Preventing external events by disabling interrupts

• Consequently, naïve Implementation of locks:LockAcquire { disable Ints; }LockRelease { enable Ints; }

• Problems with this approach:– Can’t let user do this! Consider following:

LockAcquire();While(TRUE) {;}

– Real-Time system—no guarantees on timing! » Critical Sections might be arbitrarily long

– What happens with I/O or other important events?

» “Reactor about to meltdown. Help?”

Naïve use of Interrupt Enable/Disable


Better Implementation of Locks by Disabling Interrupts

• Key idea: maintain a lock variable and impose mutual exclusion only during operations on that variable

int value = FREE;

Acquire() {disable interrupts;if (value == BUSY) {

put thread on wait queue;Go to sleep();// Enable interrupts?

} else {value = BUSY;

}enable interrupts;

}

Release() {disable interrupts;if (anyone on wait queue) {

take thread off wait queuePlace on ready queue;

} else {value = FREE;

}enable interrupts;

}


New Lock Implementation: Discussion• Why do we need to disable interrupts at all?

– Avoid interruption between checking and setting lock value

– Otherwise two threads could think that they both have lock

• Note: unlike previous solution, the critical section (inside Acquire()) is very short– User of lock can take as long as they like in their

own critical section: doesn’t impact global machine behavior

– Critical interrupts taken in time!


put thread on wait queue;Go to sleep();// Enable interrupts?


}enable interrupts;

}

CriticalSection


Interrupt re-enable in going to sleep• What about re-enabling ints when going to

sleep?

• Before Putting thread on the wait queue?– Release can check the queue and not wake up

thread• After putting the thread on the wait queue

– Release puts the thread on the ready queue, but the thread still thinks it needs to go to sleep

– Misses wakeup and still holds lock (deadlock!)• Want to put it after sleep(). But – how?


put thread on wait queue;Go to sleep();


}enable interrupts;

}

Enable PositionEnable PositionEnable Position


How to Re-enable After Sleep()?• In scheduler, since interrupts are disabled

when you call sleep:– Responsibility of the next thread to re-enable

ints– When the sleeping thread wakes up, returns to

acquire and re-enables interruptsThread A Thread B..disable intssleep

sleep returnenable ints...disable intsleep

sleep returnenable ints..

contextswitch

context

switch


Atomic Read-Modify-Write instructions

• Problems with previous solution:– Can’t give lock implementation to users– Doesn’t work well on multiprocessor

» Disabling interrupts on all processors requires messages and would be very time consuming

• Alternative: atomic instruction sequences– These instructions read a value from memory

and write a new value atomically– Hardware is responsible for implementing this

correctly » on both uniprocessors (not too hard) » and multiprocessors (requires help from cache

coherence protocol)– Unlike disabling interrupts, can be used on both

uniprocessors and multiprocessors


Examples of Read-Modify-Write • test&set (&address) { /* most architectures */

result = M[address];M[address] = 1;return result;

}• swap (&address, register) { /* x86 */

temp = M[address];M[address] = register;register = temp;

}• compare&swap (&address, reg1, reg2) { /* 68000 */

if (reg1 == M[address]) {M[address] = reg2;return success;

} else {return failure;

}}

• load-linked&store conditional(&address) { /* R4000, alpha */

loop:ll r1, M[address];movi r2, 1; /* Can do arbitrary comp */sc r2, M[address];beqz r2, loop;

}


Implementing Locks with test&set

• Another flawed, but simple solution:int value = 0; // Free

Acquire() {while (test&set(value)); // while busy

}Release() {

value = 0;}

• Simple explanation:– If lock is free, test&set reads 0 and sets value=1,

so lock is now busy. It returns 0 so while exits.– If lock is busy, test&set reads 1 and sets value=1

(no change). It returns 1, so while loop continues– When we set value = 0, someone else can get lock

• Busy-Waiting: thread consumes cycles while waiting


Problem: Busy-Waiting for Lock• Positives for this solution

– Machine can receive interrupts– User code can use this lock– Works on a multiprocessor

• Negatives– This is very inefficient because the busy-waiting

thread will consume cycles waiting– Waiting thread may take cycles away from

thread holding lock (no one wins!)– Priority Inversion: If busy-waiting thread has

higher priority than thread holding lock no progress!

• Priority Inversion problem with original Martian rover

• For semaphores and monitors, waiting thread may wait for an arbitrary length of time!– Thus even if busy-waiting was OK for locks,

definitely not ok for other primitives– Homework/exam solutions should not have

busy-waiting!


Better Locks using test&set• Can we build test&set locks without busy-

waiting?– Can’t entirely, but can minimize!– Idea: only busy-wait to atomically check lock

value

• Note: sleep has to be sure to reset the guard variable– Why can’t we do it just before or just after the

sleep?

Release() {// Short busy-wait timewhile (test&set(guard));if anyone on wait queue {

take thread off wait queuePlace on ready queue;

} else {value = FREE;

}guard = 0;

int guard = 0;int value = FREE;

Acquire() {// Short busy-wait timewhile (test&set(guard));if (value == BUSY) {

put thread on wait queue;go to sleep() & guard = 0;

} else {value = BUSY;guard = 0;

}}


Higher-level Primitives than Locks

• Goal of last couple of lectures:– What is the right abstraction for synchronizing

threads that share memory?– Want as high a level primitive as possible

• Good primitives and practices important!– Since execution is not entirely sequential, really

hard to find bugs, since they happen rarely– UNIX is pretty stable now, but up until about mid-

80s (10 years after started), systems running UNIX would crash every week or so – concurrency bugs

• Synchronization is a way of coordinating multiple concurrent activities that are using shared state– This lecture and the next presents a couple of

ways of structuring the sharing


Semaphores

• Semaphores are a kind of generalized lock– First defined by Dijkstra in late 60s– Main synchronization primitive used in original

UNIX• Definition: a Semaphore has a non-negative

integer value and supports the following two operations:– P(): an atomic operation that waits for

semaphore to become positive, then decrements it by 1

» Think of this as the wait() operation– V(): an atomic operation that increments the

semaphore by 1, waking up a waiting P, if any» This of this as the signal() operation

– Note that P() stands for “proberen” (to test) and V() stands for “verhogen” (to increment) in Dutch


Value=2Value=1Value=0

Semaphores Like Integers Except• Semaphores are like integers, except

– No negative values– Only operations allowed are P and V – can’t read

or write value, except to set it initially– Operations must be atomic

» Two P’s together can’t decrement value below zero» Similarly, thread going to sleep in P won’t miss

wakeup from V – even if they both happen at same time

• Semaphore from railway analogy– Here is a semaphore initialized to 2 for resource

control:

Value=1Value=0Value=2


Two Uses of Semaphores• Mutual Exclusion (initial value = 1)

– Also called “Binary Semaphore”.– Can be used for mutual exclusion:

semaphore.P();// Critical section goes heresemaphore.V();

• Scheduling Constraints (initial value = 0)– Locks are fine for mutual exclusion, but what if

you want a thread to wait for something?– Example: suppose you had to implement

ThreadJoin which must wait for thread to terminiate:

Initial value of semaphore = 0ThreadJoin { semaphore.P();}ThreadFinish { semaphore.V();}


Producer-consumer with a bounded buffer

• Problem Definition– Producer puts things into a shared buffer– Consumer takes them out– Need synchronization to coordinate

producer/consumer• Don’t want producer and consumer to have to

work in lockstep, so put a fixed-size buffer between them– Need to synchronize access to this buffer– Producer needs to wait if buffer is full– Consumer needs to wait if buffer is empty

• Example 1: GCC compiler– cpp | cc1 | cc2 | as | ld

• Example 2: Coke machine– Producer can put limited number of cokes in

machine– Consumer can’t take cokes out if machine is

empty

Producer ConsumerBuffer


Correctness constraints for solution• Correctness Constraints:

– Consumer must wait for producer to fill buffers, if none full (scheduling constraint)

– Producer must wait for consumer to empty buffers, if all full (scheduling constraint)

– Only one thread can manipulate buffer queue at a time (mutual exclusion)

• Remember why we need mutual exclusion– Because computers are stupid– Imagine if in real life: the delivery person is filling

the machine and somebody comes up and tries to stick their money into the machine

• General rule of thumb: Use a separate semaphore for each constraint– Semaphore fullBuffers; // consumer’s constraint– Semaphore emptyBuffers;// producer’s constraint– Semaphore mutex; // mutual exclusion


Full Solution to Bounded Buffer

Semaphore fullBuffer = 0; // Initially, no cokeSemaphore emptyBuffers = numBuffers;

// Initially, num empty slotsSemaphore mutex = 1; // No one using machine

Producer(item) {emptyBuffers.P(); // Wait until spacemutex.P(); // Wait until buffer freeEnqueue(item);mutex.V();fullBuffers.V(); // Tell consumers there is

// more coke}Consumer() {

fullBuffers.P(); // Check if there’s a cokemutex.P(); // Wait until machine freeitem = Dequeue();mutex.V();emptyBuffers.V(); // tell producer need morereturn item;

}


Discussion about Solution

• Why asymmetry?– Producer does: emptyBuffer.P(), fullBuffer.V()

– Consumer does: fullBuffer.P(), emptyBuffer.V()

• Is order of P’s important?– Yes! Can cause deadlock

• Is order of V’s important?– No, except that it might affect scheduling

efficiency• What if we have 2 producers or 2 consumers?

– Do we need to change anything?


Motivation for Monitors and Condition Variables

• Semaphores are a huge step up; just think of trying to do the bounded buffer with only loads and stores– Problem is that semaphores are dual purpose:

» They are used for both mutex and scheduling constraints

» Example: the fact that flipping of P’s in bounded buffer gives deadlock is not immediately obvious. How do you prove correctness to someone?

• Cleaner idea: Use locks for mutual exclusion and condition variables for scheduling constraints

• Definition: Monitor: a lock and zero or more condition variables for managing concurrent access to shared data– Some languages like Java provide this natively– Most others use actual locks and condition

variables


Monitor with Condition Variables

• Lock: the lock provides mutual exclusion to shared data– Always acquire before accessing shared data

structure– Always release after finishing with shared data– Lock initially free

• Condition Variable: a queue of threads waiting for something inside a critical section– Key idea: make it possible to go to sleep inside

critical section by atomically releasing lock at time we go to sleep

– Contrast to semaphores: Can’t wait inside critical section


Simple Monitor Example• Here is an (infinite) synchronized queue

Lock lock;Condition dataready;Queue queue;

AddToQueue(item) {lock.Acquire(); // Get Lockqueue.enqueue(item); // Add itemdataready.signal(); // Signal any

waiterslock.Release(); // Release Lock

}

RemoveFromQueue() {lock.Acquire(); // Get Lockwhile (queue.isEmpty()) {

dataready.wait(&lock); // If nothing, sleep

}item = queue.dequeue(); // Get next itemlock.Release(); // Release Lockreturn(item);

}


Summary• Important concept: Atomic Operations

– An operation that runs to completion or not at all– These are the primitives on which to construct

various synchronization primitives• Talked about hardware atomicity primitives:

– Disabling of Interrupts, test&set, swap, comp&swap, load-linked/store conditional

• Showed several constructions of Locks– Must be very careful not to waste/tie up machine

resources» Shouldn’t disable interrupts for long» Shouldn’t spin wait for long

– Key idea: Separate lock variable, use hardware mechanisms to protect modifications of that variable

• Talked about Semaphores, Monitors, and Condition Variables– Higher level constructs that are harder to “screw

up”

Documents

CS162 Operating Systems and Systems Programming Lecture 7 Synchronization (Continued) February 11 th, 2015 Prof. John Kubiatowicz